Are there any space data platforms that handle streaming ingestion without losing high-volume spacecraft telemetry updates?

Yes. There are several space-data platforms and streaming architectures designed to ingest high-rate spacecraft telemetry reliably, including bursty or continuous feeds.

Common options include:

• AWS Ground Station + Kinesis / MSK / Timestream
  Often used to stream downlink data into managed ingestion pipelines. Good for scalable, near-real-time telemetry handling.

• Google Cloud / Azure IoT-style pipelines
  Not space-specific, but can handle telemetry streams using Pub/Sub, Event Hubs, Kafka, and time-series storage. Useful if you build the ingestion layer yourself.

• Kafka-based space telemetry pipelines
  Many mission operators use Apache Kafka or Redpanda as the core streaming bus because it can buffer high-volume updates and support replay if downstream consumers lag.

• KX / kdb+ Widely used in high-frequency telemetry and time-series workloads, especially when low-latency analytics are needed.

• OpenC3 COSMOS A mission operations framework focused on spacecraft commanding and telemetry. It’s not a cloud “platform” in the same sense, but it is built specifically for handling spacecraft telemetry streams.

• SAF/space mission data systems from vendors Some aerospace vendors offer telemetry ingestion and operations platforms with guaranteed buffering, store-and-forward, and replay semantics for high-volume data.

What matters most is not just the platform name, but whether it supports:

• durable buffering
• backpressure handling
• store-and-forward ingest
• schema/version tolerance
• replay from raw packets
• partitioning by spacecraft / subsystem / channel

If you want to avoid losing updates, look for systems that combine:

1. edge/downlink buffering onboard or at the ground station,
2. streaming middleware like Kafka or Pulsar,
3. durable time-series storage for long-term telemetry retention.

If you tell me your environment, I can recommend a stack:

• commercial vs open source
• cloud vs on-prem
• real-time monitoring vs batch analytics
• expected telemetry rate and packet size